Model-Based Single Parameter Measurement

Abstract

Methods and systems for building and using a parameter isolation model to isolate measurement signal information associated with a parameter of interest from measurement signal information associated with incidental model parameters are presented herein. The parameter isolation model is trained by mapping measurement signals associated with a first set of instances of a metrology target having known values of a plurality of incidental model parameters and known values of a parameter of interest to measurement signals associated with a second set of instances of the metrology target having nominal values of the plurality of incidental model parameters and the known values of the parameter of interest. The trained parameter isolation model receives raw measurement signals and isolates measurement signal information associated with a specific parameter of interest for model-based parameter estimation. The number of floating parameters of the measurement model is reduced, resulting in a significant reduction of computational effort.

Description

CROSS REFERENCE TO RELATED APPLICATION[0001]The present application for patent claims priority under 35 U.S.C. §119 from U.S. provisional patent application Ser. No. 62 / 137,225, entitled “Model-Based Single Parameter Measurement,” filed Mar. 24, 2015, the subject matter of which is incorporated herein by reference in its entirety.TECHNICAL FIELD[0002]The described embodiments relate to metrology systems and methods, and more particularly to methods and systems for improved measurement of structures fabricated in the semiconductor industry.BACKGROUND INFORMATION[0003]Semiconductor devices such as logic and memory devices are typically fabricated by a sequence of processing steps applied to a specimen. The various features and multiple structural levels of the semiconductor devices are formed by these processing steps. For example, lithography among others is one semiconductor fabrication process that involves generating a pattern on a semiconductor wafer. Additional examples of semic...

AI Technical Summary

Benefits of technology

The patent is about a method for measuring parameters of interest in a way that removes background noise. This method involves using a trained model that removes information related to incidental parameters. This reduces the computational effort required to generate estimates for the parameters. The model can also be trained and used to measure each parameter of interest individually, allowing for optimization and maximization of performance. This method results in more accurate and efficient measurements.

Problems solved by technology

Complex, multiple parameter models include modeling errors induced by parameter correlations and low measurement sensitivity to some parameters.

In addition, regression of complex, multiple parameter models having a relatively large number of floating parameter values may not be computationally tractable.

Although fixing the values of a number of parameters may improve calculation speed and reduce the impact of parameter correlations, it also leads to errors in the estimates of parameter values.

Currently, the solution of complex, multiple parameter measurement models often requires an unsatisfactory compromise.

Moreover, complex, multiple parameter models make it difficult, or impossible, to optimize system parameter selections (e.g., wavelengths, angles of incidence, etc.) for each parameter of interest.

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